Robust Broad Learning System with Wave Loss for Classification under Data Uncertainty

2026-08-30

AI Summary

为解决BLS在数据不确定性下的敏感性问题，提出Wave-BLS框架，采用波损失函数并通过NAG方案优化，提高鲁棒性和稳定性。

Abstract

Broad Learning System (BLS) offers an efficient alternative to deep architectures by enabling fast learning through randomized feature mapping and closed-form solutions. However, its reliance on squared error loss makes it highly sensitive to noise, outliers, and corrupted labels, limiting its reliability in real-world scenarios. To address this limitation, we propose Wave-BLS, a robust broad learning framework that integrates the wave loss function, which is asymmetric, bounded, and smooth, enabling controlled penalization of large errors. The proposed formulation replaces the standard least-squares objective with a wave-loss-based optimization problem, solved efficiently using a Nesterov accelerated gradient (NAG)-based scheme without requiring matrix inversion, thereby improving scalability. Extensive experiments on 30 UCI benchmark datasets demonstrate that Wave-BLS consistently outperforms classical BLS and several robust variants. Statistical validation using Friedman and Nemenyi post-hoc tests confirms the significance of the observed improvements. Furthermore, robustness evaluations under controlled noise and outlier injection reveal that Wave-BLS exhibits substantially slower performance degradation compared to BLS, even in challenging contamination settings. These results establish Wave-BLS as a stable and robust alternative to existing broad learning models for learning under data uncertainty.

Problem

• Broad Learning System
• Data Uncertainty
• Robustness
• Noise Sensitivity
• Outliers

Innovation

• Wave-BLS
• wave loss function
• Nesterov accelerated gradient (NAG)
• robustness
• data uncertainty